A Socially Assistive Robot for Elderly Exercise Promotion

Abstract

The population ageing phenomenon leads to an unceasing need for home-based healthcare systems to continuously monitor the elderly's cognitive and physical health. In this sense, physical activity may be beneficial in preserving cognition in elder life as well as in providing clinicians and therapists with the indicative of elderly's health condition. Nevertheless, current systems fail to promote and monitor the elderly's physical activity in their living environments. This paper is aimed at providing a socially assistive robot solution for this task. Since robot acceptance depends to a great extent on its robustness in performing tasks, we have focused on exercise recognition due to its great importance for both clinicians and elderly. For that, two different tasks were carried out. First, an image dataset for physical exercise recognition has been generated. Then, a comparative analysis of several deep learning techniques is presented. This paper reveals a great performance in the exercise recognition of CNN-LSTM with an exercise recognition accuracy of 99.87%.

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

“…Furthermore, Martinez et al [ 57 ] proposed an algorithm for the Pepper robot to promote and monitor older adults’ physical activity in their living environments. They used an RGB-D camera to extract the skeleton joints, and then a double-layer network (CNN and LSTM) through the OpenPose framework recognized up to 24 different exercises.…”

“…On the other hand, among the algorithms that were implemented to monitor patients during exercise, the authors of [ 57 ] reported 99.87% performance, using a CNN and an LSTM deployed in a Pepper robot with a Kinect sensor. However, other authors reported performance greater than 95%; they used other robotic platforms such as the NAO robot and the Poppy robot.…”

A Low-Cost Cognitive Assistant

“…One example is the work of Martinez-Martin et al [23][24][25], which proposed a rehabilitation system to provide rehabilitation monitoring at home using a humanoid robot. The goal was to use the robot's cameras to access the user's physical movements visually, using deep learning methods, and correct them using the robot screen and body to convey this information.…”

PHAROS 2.0—A PHysical Assistant RObot System Improved

“…Unlike CNNs, RNNs make decisions based on both the current and the previous input. Nevertheless, an experimental analysis presented in Reference [44] highlights that a combination of a CNN followed by a RNN provides better accuracy in learning the temporal sequence corresponding to physical exercises than a just RNN architecture.…”